Challenge

Project 1

VVignesh S4 Dec 2023

Hi,

The code for the above project is mentioned below and also attached the file in the attachment.

CODE:

# Import libraries

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler

from sklearn.linear_model import LogisticRegression

from sklearn.neighbors import KNeighborsClassifier

from sklearn.svm import SVC

from sklearn.tree import DecisionTreeClassifier

from sklearn.ensemble import RandomForestClassifier

from sklearn.metrics import accuracy_score, classification_report

# Load the dataset

data = pd.read_csv("heart.csv")

data

# Step 1: Data Preprocessing

# Drop rows with missing values (if any)

data.dropna(inplace=True)

data.isnull().count()

# features (X) and target variable (y)

X = data.drop('output', axis=1)

y = data['output']

print(X)

print("===========================================================================")

print(y)

# Step 2: Data Splitting

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Step 3: Model Training

# Logistic Regression

logreg = LogisticRegression()

logreg.fit(X_train, y_train)

# K-Nearest Neighbours Classifier

knn = KNeighborsClassifier()

knn.fit(X_train, y_train)

# Support Vector Machine

svc = SVC()

svc.fit(X_train, y_train)

# Decision Tree Classifier

dtc = DecisionTreeClassifier()

dtc.fit(X_train, y_train)

# Random Forest Classifier

rfc = RandomForestClassifier()

rfc.fit(X_train, y_train)

# Step 4: Model Prediction

y_predict_logreg = logreg.predict(X_test)

y_predict_knn = knn.predict(X_test)

y_predict_svc = svc.predict(X_test)

y_predict_dtc = dtc.predict(X_test)

y_predict_rfc = rfc.predict(X_test)

# Step 5: Model Evaluation

# Logistic Regression

accuracy_logreg = accuracy_score(y_test, y_predict_logreg)

print("Logistic Regression Accuracy:", accuracy_logreg)